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Learning spatial cellular motifs predictive of the responses of patients to cancer treatments

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Graph deep learning applied to multiplexed immunofluorescence data from tumour microenvironments reveals spatial cellular structures that are indicative of cancer prognosis.

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Fig. 1: SPACE-GM identifies key spatial structures from graphs.

References

  1. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018). This paper presents CODEX, the multiplexed immunofluorescence imaging method used in this work.

    Article  CAS  Google Scholar 

  2. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020); erratum 183, 838 (2020). This paper presents the cellular neighbourhood method for analysing multiplexed immunofluorescence imaging.

  3. Argiris, A., Karamouzis, M. V., Raben, D. & Ferris, R. L. Head and neck cancer. Lancet 371, 1695–1709 (2008). A review article that presents an overview of head-and-neck cancer.

    Article  CAS  Google Scholar 

  4. Blise, K. E., Sivagnanam, S., Banik, G. L., Coussens, L. M. & Goecks, J. Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma. NPJ Precis. Oncol. 6, 10 (2022). This paper discusses tumour–immune cell spatial compartmentalization in head-and-neck squamous cell carcinoma.

    Article  CAS  Google Scholar 

  5. Trellakis, S. et al. Polymorphonuclear granulocytes in human head and neck cancer: enhanced inflammatory activity, modulation by cancer cells and expansion in advanced disease. Int. J. Cancer 129, 2183–2193 (2011). This paper discusses the role of tumour-infiltrating polymorphonuclear granulocytes in head-and-neck squamous cell carcinoma.

    Article  CAS  Google Scholar 

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This is a summary of: Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00951-w (2022).

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Learning spatial cellular motifs predictive of the responses of patients to cancer treatments. Nat. Biomed. Eng 6, 1328–1329 (2022). https://doi.org/10.1038/s41551-022-00958-3

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